(row, ret_doc=False)
| 113 | |
| 114 | |
| 115 | def nlp_processing(row, ret_doc=False): |
| 116 | tickers_ment = row['tickers'] |
| 117 | |
| 118 | if(len(tickers_ment) == 0): |
| 119 | return None, None |
| 120 | |
| 121 | sentence = row['body'].strip() |
| 122 | |
| 123 | tk_spans = [] |
| 124 | |
| 125 | doc = nlp(sentence) |
| 126 | |
| 127 | sid = SentimentIntensityAnalyzer() |
| 128 | sentiment = sid.polarity_scores(doc.text) |
| 129 | |
| 130 | if(len(tickers_ment) > 1): |
| 131 | for ticker in tickers_ment: |
| 132 | pos = doc.text.find(ticker) |
| 133 | span = doc.char_span(pos, pos + len(ticker), label="ORG") |
| 134 | try: |
| 135 | doc.ents = [span if e.text == ticker else e for e in doc.ents] |
| 136 | except Exception as e: |
| 137 | print(e) |
| 138 | |
| 139 | return doc, sentiment |
| 140 | |
| 141 | |
| 142 | def filter_hour(row, day=2): |
nothing calls this directly
no outgoing calls
no test coverage detected